Date of Award
Mathematics and Statistics
A common multivariate statistical problem is the prediction of two or more response variables using two or more predictor variables. The simplest model for this situation is the multivariate linear regression model. The standard least squares estimation for this model involves regressing each response variable separately on all the predictor variables. Breiman and Friedman  show how to take advantage of correlations among the response variables to increase the predictive accuracy for each of the response variable with an algorithm they call Curds and Whey. In this report, I describe an implementation of the Curds and Whey algorithm in the R language and environment for statistical computing , apply the algorithm to some example data sets, and discuss extensions of the algorithm to linear classification methods.
Kidd, John Cameron, "Curds and Whey: Little Miss Muffit's Contribution to Multivariate Linear Regression" (2013). Undergraduate Honors Capstone Projects. 138.
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